[kwen2501]

Motivation

Previously we have this block of code in RowwiseParallel:

# wait for the output to be ready
if isinstance(outputs, AsyncCollectiveTensor):
  return outputs.wait()
else:
  return outputs

When dynamo traces, outputs is an AsyncCollectiveTensor, so it burns in the outputs.wait() call. But at run time, somehow outputs become a normal tensor, thus we hit the following error:

AttributeError: 'FunctionalTensor' object has no attribute 'wait'

This is a bug in Dynamo.

Modifications

To work around the above Dynamo bug, we swap the above if-else block with a determined behavior:

torch.distributed._functional_collectives.wait_tensor(outputs)

wait_tensor accepts both regular tensor and AsyncCollectiveTensor. How it handles them is implementation detail it keeps inside. This would work in both eager and compiled mode.

Checklist

• Format your code according to the Contributor Guide.
• Add unit tests as outlined in the Contributor Guide.
• Update documentation as needed, including docstrings or example tutorials.

cc @jerryzh168 @bdhirsh

[bdhirsh]

Here's a minimal repro:

diff --git a/test/distributed/_tensor/test_dtensor_compile.py b/test/distributed/_tensor/test_dtensor_compile.py
index 91fbc396f8e..09a2bf8f183 100644
--- a/test/distributed/_tensor/test_dtensor_compile.py
+++ b/test/distributed/_tensor/test_dtensor_compile.py
@@ -544,12 +544,18 @@ class TestDTensorCompile(torch._dynamo.test_case.TestCase):

     def test_dynamo_dtensor_from_local_redistribute(self):
         mesh = DeviceMesh(self.device_type, torch.arange(self.world_size))
+        from torch.distributed._functional_collectives import AsyncCollectiveTensor

         # pass in tensor as inputs/outputs, create DTensor and run redistribute
         # (allgather collective) inside the fn
         def fn(x):
             dt = DTensor.from_local(x, mesh, [Shard(0)], run_check=False)
-            return dt.redistribute(mesh, [Replicate()]).to_local() + 2
+            out = dt.redistribute(mesh, [Replicate()], async_op=True).to_local()
+            return out
+            if isinstance(out, AsyncCollectiveTensor):
+                return out.wait()
+            else:
+                return out

         x = torch.ones(1)
         ref = fn(x)

[bdhirsh]

Fixed in core with pytorch/pytorch#142075